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Reducing offline evaluation bias of collaborative filtering algorithms
[article]
2015
arXiv
pre-print
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This paper presents a new application of a weighted offline evaluation to reduce this bias for collaborative filtering algorithms. ...
It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical data (via offline evaluation ...
Thus, this method seems to reduce the bias for the very simple class of constant algorithms. In the next part we apply this method to collaborative filtering algorithms. ...
arXiv:1506.04135v1
fatcat:osqalsytu5gbrdpt5jbjhhs6ai
Study of a bias in the offline evaluation of a recommendation algorithm
[article]
2015
arXiv
pre-print
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This paper describes this bias and discuss the relevance of a weighted offline evaluation to reduce this bias for different classes of recommendation algorithms. ...
It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical data (via offline evaluation ...
In the next part we discuss the relevance of this procedure to reduce the offline evaluation bias on collaborative filtering algorithms. ...
arXiv:1511.01280v1
fatcat:s4j3pggganfibk4ninktcssubu
BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System
[article]
2021
arXiv
pre-print
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BanditMF is designed to address two challenges in the multi-armed bandits algorithm and collaborative filtering: (1) how to solve the cold start problem for collaborative filtering under the condition ...
For collaborative filtering, the classical method is training the model offline, then perform the online testing, but this approach can no longer handle the dynamic changes in user preferences which is ...
To compare the performance between the base method and bias method, Mean Square Error (MSE) is used to evaluate these two algorithms. ...
arXiv:2106.10898v2
fatcat:upnm5pmva5agvenkcuolwv5muq
Learning to Rank Research Articles: A Case Study of Collaborative Filtering and Learning to Rank in ScienceDirect
2020
International Workshop on Bibliometric-enhanced Information Retrieval
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We then describe offline and online evaluation, which are essential for productionizing any recommender. ...
We first introduce itemto-item collaborative filtering (CF), then how these recommendations are rescored with a LtR model. ...
Online evaluation Our best candidate algorithms from offline evaluation are compared against the current production model via A/B testing. ...
dblp:conf/birws/KershawPHJ20
fatcat:lhqmte2j3ba7tfax2dws47jmiy
RecSys for distributed events
2013
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '13
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There are often a large number of events on offer and the times in which they can be visited are heavily constrained, therefore the task of choosing events to visit and in which order can be very difficult ...
Distributed events are collections of events taking place within a small area over the same time period and relating to a single topic. ...
A modern, state-of-the-art collaborative filtering algorithm [2] . ...
doi:10.1145/2484028.2484119
dblp:conf/sigir/SchallerHE13
fatcat:hqmjacuinncedmo2e6udpcdp54
Triggering on hard probes in heavy-ion collisions with CMS
2007
Journal of Physics G: Nuclear and Particle Physics
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The study is based on measurements of the timing performance of the offline algorithms and event-size distributions using full simulations. ...
Concentrating on two physics channels, dimuons from decays of quarkonia and single jets, we evaluate a possible trigger strategy for Pb+Pb running that relies on event selection solely in the High-Level ...
All rejection of Pb+Pb collisions will be based on the outcome of HLT trigger algorithms that are identical to the corresponding offline algorithms or optimized versions of the offline algorithms. ...
doi:10.1088/0954-3899/34/8/s84
fatcat:aqnn2xfyqzdfretbstp5eyqzjq
Microsoft recommenders
2019
Proceedings of the 13th ACM Conference on Recommender Systems - RecSys '19
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2020 pre-print arXiv:2008.13528v1 fatcat:tfueqpav6vholduxxw4peqpera
The purpose of this work is to highlight the content of the Microsoft Recommenders repository and show how it can be used to reduce the time involved in developing recommender systems. ...
The open source repository provides python utilities to simplify common recommender-related data science work as well as example Jupyter notebooks that demonstrate use of the algorithms and tools under ...
Special thanks to Andreas Argyriou, Miguel González-Fierro, and Le Zhang for their work in the development of the repository. ...
doi:10.1145/3298689.3346967
dblp:conf/recsys/GrahamMW19
fatcat:akiqjj2qbvaxphhu6ttamyfpsq
Personalization in E-Grocery: Top-N versus Top-k Rankings
[article]
2021
arXiv
pre-print
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In e-grocery, shoppers face an overwhelming number of items, the majority of which is irrelevant for the shopper. ...
Specifically, the proposed RS reduces IO by 29.4% and lowers users' search time by 3.3 seconds per item. The field experiment also reveals a 7% uplift in revenue due to the top-N ranking. ...
The developers of COUSIN also demonstrate its superiority over other collaborative filtering methods in an offline evaluation (Gan 2015) . ...
arXiv:2105.14599v1
fatcat:izmqwdfh2naanfgxbbls7kry7i
Selecting Appropriate Metrics for Evaluation of Recommender Systems
2019
International Journal of Information Technology and Computer Science
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However, with the availability of several recommender tasks, recommender algorithms, and evaluation metrics, it is often difficult for a researcher to find their best combination. ...
This paper aims to discuss various evaluation metrics in order to help researchers to select the most appropriate metric which matches a given task and an algorithm so as to provide good quality of recommendations ...
Offline Experiments The objective of the offline experiment is to select the most appropriate recommendation approaches and filter out the irrelevant approaches, which reduces the list of candidate algorithms ...
doi:10.5815/ijitcs.2019.01.02
fatcat:df2ar6nsj5dh7an7bdvlj4wed4
An Educational News Dataset for Recommender Systems
[chapter]
2020
Communications in Computer and Information Science
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We discuss the structure and purpose of the refined dataset in this paper. ...
AbstractDatasets are an integral part of contemporary research on recommender systems. ...
This work was carried out as part of the industry-led research project RecTech, project number 245469, supported by the Research Council of Norway's BIA innovation research program. ...
doi:10.1007/978-3-030-65965-3_39
fatcat:7e5lmfw4jbc4bgrzolu7iipste
Metaphor
2012
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
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Metaphor builds on a number of signals and filters that capture several dimensions of relatedness across member search activity. ...
Third, we introduce a query length model for capturing bias in recommendation click behavior. We also discuss some of the practical concerns in deploying related search recommendations. ...
Offline Evaluation Offline evaluation provides a scientific and easily repeatable mechanism for establishing performance and tuning parameters. ...
doi:10.1145/2396761.2396847
dblp:conf/cikm/RedaPTPS12
fatcat:4nttbjperngi5dj46fjtivsswe
A User Trust-Based Collaborative Filtering Recommendation Algorithm
[chapter]
2009
Lecture Notes in Computer Science
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The values of trust among users are adjusted by using the reinforcement learning algorithm. On the basis of this, a user trust-based collaborative filtering recommendation algorithm is proposed. ...
Experimental results show that the proposed algorithm outperforms the traditional user-based and item-based collaborative filtering algorithm in recommendation accuracy, especially in the face of malicious ...
Experimental Results To evaluate our algorithm that combined user trust with the collaborative filtering approach. ...
doi:10.1007/978-3-642-11145-7_32
fatcat:z5khsnwkpbbotlvasxbjihrbmm
Cold-start Problem in Collaborative Recommender Systems: Efficient Methods Based on Ask-to-rate Technique
2014
Journal of Computing and Information Technology
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Therefore, a major challenge of the collaborative filtering approach can be how to make recommendations for a new user, that is called cold-start user problem. ...
To develop a recommender system, the collaborative filtering is the best known approach, which considers the ratings of users who have similar rating profiles or rating patterns. ...
Collaborative Filtering Recommender Systems Collaborative filtering recommender systems are one of the biggest sub-domains of information retrieval. ...
doi:10.2498/cit.1002223
fatcat:tzrirpnk65bnbbmgaf4vuifubq
Review of Techniques for Recommender Systems
2017
International Journal of Advanced Research in Computer Science and Software Engineering
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It is determined that Bagging reduced variance of unstable methods, while boosting methods reduced both the bias and variance of unstable methods but increased the variance for NaiveBayes was very stable ...
A few algorithms are compared using some evaluation metric instead of absolute benchmarking of algorithms. The experimental settings are suggested to make decisions between algorithms. ...
doi:10.23956/ijarcsse/v7i4/0134
fatcat:kwwzlyfscnhinmfi2rsa4nzmee
Separating-Plane Factorization Models
2016
Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM '16
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With extensive offline evaluation in Tencent Data Warehouse (TDW) based on big data, we show that our approach outperforms a wide range of state-of-the-art methods. ...
Results show that our approach can increase the video click through rate by 23% over implicit-feedback collaborative filtering (IFCF), a scheme implemented in Spark's MLlib. ii ...
And the biases for each user, video and day of the week, and the global bias are taken into account. ...
doi:10.1145/2983323.2983348
dblp:conf/cikm/ChenNLXA16
fatcat:awyfcrix65ctnd6dbs5yg644uq
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